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top strategies for successful big data projects

Tim Gasper Product Manager

Infochimps, Inc.

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#1 big data platform for the cloud

some of our customers our partners

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big data data science

big data application

development

big data business

intelligence

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agenda • why you care about big data

• too many big data projects fail

• 7 strategies for success

• q&a

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poll

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BIG • volume

• velocity

• variety

• variability

• veracity

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DATA • scalable

• intelligent

• agnostic

• holistic

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oldies but goodies • website and systems

log analysis

• large scale transaction

analysis

• trade performance

analytics

• risk analysis and fraud

detection

• geo/spatial analysis

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new school • brand and sentiment analysis

• targeted marketing and

personalization

• customer insights / behavior

• big data business intelligence

• predictive modeling

• intelligent systems

• large-scale data mining and

cleansing

use cases

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44% of big data

projects are cancelled

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Infochimps/SSWUG Enterprise Big Data Survey 2012

Dynamic Markets Enterprise IT Survey 2008

and many more fail to

achieve project objectives

versus 25% for IT projects in general

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poll

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why do projects fail?

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• business • inaccurate scope

• not enough time / deadlines busted

• non-cooperation between departments

• having the right talent / lack of expertise

• technical • technical or roll-out roadblocks

• gathering data from different sources

• finding and understanding tools, platforms,

technologies

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how do I prevent failure?

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mapping common challenges

to 7 simple strategies for

big data project success

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define the problem • what are your business goals

• identify the greatest

opportunities and challenges

• eat a frog

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think BIG and be SPECIFIC • what big goal are you tackling

• don’t waste your time

• is it specific enough

• is there a project

• you can’t eat an elephant in

one bite

• get to something you can

measure

• set your priorities

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identifying projects • new business initiatives

• enhancements to current

operations, systems, and

processes

• replacements for legacy

operations, systems and

processes

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map priorities to technologies • business value technology

• think high-level because the tech

is changing rapidly

• impala vs hive vs hadapt vs

vertica vs …

• batch vs real-time

• proprietary vs open

• DIY vs IaaS vs PaaS

vs SaaS

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poll

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hit the ground running • avoid analysis paralysis

• take a phased approach

• test and iterate as you go

• you’ve done your

homework, you have

your compass

• know what to do with the

data

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manage scope creep • manage requirements and

expectations

• manage complexity

• manage people creep

• manage technology creep

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embrace the cross-organizational

nature of big data • who are your initial users

• who else can benefit

• other departments

• other use cases

• other data sources,

systems, and tools

• what are the next steps

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drive business value • don’t be driven by technology, be

driven by business needs

• what is the point of big data

• NOT a cost center

• time to business value

• speed to insight

• data driven decisions

• go FAST and do AWESOME

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sales@infochimps.com

follow us: @infochimps

Let’s discuss your big data projects and ideas. infochimps.com/free-big-data-consultation

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